In [1]:
!pip install yfinance
!pip install bs4
!pip install nbformat
!pip install --upgrade plotly
!pip install lxml
Requirement already satisfied: yfinance in /opt/anaconda3/lib/python3.13/site-packages (0.2.65) Requirement already satisfied: pandas>=1.3.0 in /opt/anaconda3/lib/python3.13/site-packages (from yfinance) (2.2.3) Requirement already satisfied: numpy>=1.16.5 in /opt/anaconda3/lib/python3.13/site-packages (from yfinance) (2.1.3) Requirement already satisfied: requests>=2.31 in /opt/anaconda3/lib/python3.13/site-packages (from yfinance) (2.32.3) Requirement already satisfied: multitasking>=0.0.7 in /opt/anaconda3/lib/python3.13/site-packages (from yfinance) (0.0.12) Requirement already satisfied: platformdirs>=2.0.0 in /opt/anaconda3/lib/python3.13/site-packages (from yfinance) (4.3.7) Requirement already satisfied: pytz>=2022.5 in /opt/anaconda3/lib/python3.13/site-packages (from yfinance) (2024.1) Requirement already satisfied: frozendict>=2.3.4 in /opt/anaconda3/lib/python3.13/site-packages (from yfinance) (2.4.2) Requirement already satisfied: peewee>=3.16.2 in /opt/anaconda3/lib/python3.13/site-packages (from yfinance) (3.18.2) Requirement already satisfied: beautifulsoup4>=4.11.1 in /opt/anaconda3/lib/python3.13/site-packages (from yfinance) (4.12.3) Requirement already satisfied: curl_cffi>=0.7 in /opt/anaconda3/lib/python3.13/site-packages (from yfinance) (0.13.0) Requirement already satisfied: protobuf>=3.19.0 in /opt/anaconda3/lib/python3.13/site-packages (from yfinance) (5.29.3) Requirement already satisfied: websockets>=13.0 in /opt/anaconda3/lib/python3.13/site-packages (from yfinance) (15.0.1) Requirement already satisfied: soupsieve>1.2 in /opt/anaconda3/lib/python3.13/site-packages (from beautifulsoup4>=4.11.1->yfinance) (2.5) Requirement already satisfied: cffi>=1.12.0 in /opt/anaconda3/lib/python3.13/site-packages (from curl_cffi>=0.7->yfinance) (1.17.1) Requirement already satisfied: certifi>=2024.2.2 in /opt/anaconda3/lib/python3.13/site-packages (from curl_cffi>=0.7->yfinance) (2025.7.14) Requirement already satisfied: pycparser in /opt/anaconda3/lib/python3.13/site-packages (from cffi>=1.12.0->curl_cffi>=0.7->yfinance) (2.21) Requirement already satisfied: python-dateutil>=2.8.2 in /opt/anaconda3/lib/python3.13/site-packages (from pandas>=1.3.0->yfinance) (2.9.0.post0) Requirement already satisfied: tzdata>=2022.7 in /opt/anaconda3/lib/python3.13/site-packages (from pandas>=1.3.0->yfinance) (2025.2) Requirement already satisfied: six>=1.5 in /opt/anaconda3/lib/python3.13/site-packages (from python-dateutil>=2.8.2->pandas>=1.3.0->yfinance) (1.17.0) Requirement already satisfied: charset-normalizer<4,>=2 in /opt/anaconda3/lib/python3.13/site-packages (from requests>=2.31->yfinance) (3.3.2) Requirement already satisfied: idna<4,>=2.5 in /opt/anaconda3/lib/python3.13/site-packages (from requests>=2.31->yfinance) (3.7) Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/anaconda3/lib/python3.13/site-packages (from requests>=2.31->yfinance) (2.3.0) Requirement already satisfied: bs4 in /opt/anaconda3/lib/python3.13/site-packages (0.0.2) Requirement already satisfied: beautifulsoup4 in /opt/anaconda3/lib/python3.13/site-packages (from bs4) (4.12.3) Requirement already satisfied: soupsieve>1.2 in /opt/anaconda3/lib/python3.13/site-packages (from beautifulsoup4->bs4) (2.5) Requirement already satisfied: nbformat in /opt/anaconda3/lib/python3.13/site-packages (5.10.4) Requirement already satisfied: fastjsonschema>=2.15 in /opt/anaconda3/lib/python3.13/site-packages (from nbformat) (2.20.0) Requirement already satisfied: jsonschema>=2.6 in /opt/anaconda3/lib/python3.13/site-packages (from nbformat) (4.23.0) Requirement already satisfied: jupyter-core!=5.0.*,>=4.12 in /opt/anaconda3/lib/python3.13/site-packages (from nbformat) (5.7.2) Requirement already satisfied: traitlets>=5.1 in /opt/anaconda3/lib/python3.13/site-packages (from nbformat) (5.14.3) Requirement already satisfied: attrs>=22.2.0 in /opt/anaconda3/lib/python3.13/site-packages (from jsonschema>=2.6->nbformat) (24.3.0) Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /opt/anaconda3/lib/python3.13/site-packages (from jsonschema>=2.6->nbformat) (2023.7.1) Requirement already satisfied: referencing>=0.28.4 in /opt/anaconda3/lib/python3.13/site-packages (from jsonschema>=2.6->nbformat) (0.30.2) Requirement already satisfied: rpds-py>=0.7.1 in /opt/anaconda3/lib/python3.13/site-packages (from jsonschema>=2.6->nbformat) (0.22.3) Requirement already satisfied: platformdirs>=2.5 in /opt/anaconda3/lib/python3.13/site-packages (from jupyter-core!=5.0.*,>=4.12->nbformat) (4.3.7) Requirement already satisfied: plotly in /opt/anaconda3/lib/python3.13/site-packages (6.3.0) Requirement already satisfied: narwhals>=1.15.1 in /opt/anaconda3/lib/python3.13/site-packages (from plotly) (1.31.0) Requirement already satisfied: packaging in /opt/anaconda3/lib/python3.13/site-packages (from plotly) (24.2) Requirement already satisfied: lxml in /opt/anaconda3/lib/python3.13/site-packages (5.3.0)
In [2]:
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots
In [3]:
import plotly.io as pio
pio.renderers.default = "iframe"
In [4]:
import warnings
# Ignore all warnings
warnings.filterwarnings("ignore", category=FutureWarning)
In [5]:
def make_graph(stock_data, revenue_data, stock):
fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
stock_data_specific = stock_data[stock_data.Date <= '2021-06-14']
revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
fig.update_xaxes(title_text="Date", row=1, col=1)
fig.update_xaxes(title_text="Date", row=2, col=1)
fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
fig.update_layout(showlegend=False,
height=900,
title=stock,
xaxis_rangeslider_visible=True)
fig.show()
from IPython.display import display, HTML
fig_html = fig.to_html()
display(HTML(fig_html))
Question 1: Use yfinance to Extract Stock Data
In [6]:
tesla=yf.Ticker('TSLA')
In [7]:
tesla_data = tesla.history(period='max')
In [8]:
tesla_data.reset_index(inplace=True)
tesla_data.head(5)
Out[8]:
| Date | Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|---|
| 0 | 2010-06-29 00:00:00-04:00 | 1.266667 | 1.666667 | 1.169333 | 1.592667 | 281494500 | 0.0 | 0.0 |
| 1 | 2010-06-30 00:00:00-04:00 | 1.719333 | 2.028000 | 1.553333 | 1.588667 | 257806500 | 0.0 | 0.0 |
| 2 | 2010-07-01 00:00:00-04:00 | 1.666667 | 1.728000 | 1.351333 | 1.464000 | 123282000 | 0.0 | 0.0 |
| 3 | 2010-07-02 00:00:00-04:00 | 1.533333 | 1.540000 | 1.247333 | 1.280000 | 77097000 | 0.0 | 0.0 |
| 4 | 2010-07-06 00:00:00-04:00 | 1.333333 | 1.333333 | 1.055333 | 1.074000 | 103003500 | 0.0 | 0.0 |
Question 2: Use Webscraping to Extract Tesla Revenue Data
In [9]:
URL=('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm')
html_data=requests.get(URL).text
In [10]:
soup = BeautifulSoup(html_data, 'html.parser')
In [11]:
tables=pd.read_html(str(soup))
print(f'number of tables found:{len(tables)}')
Tesla_Revenue=tables[1]
Tesla_Revenue.columns =['Date','Revenue']
number of tables found:6
In [12]:
Tesla_Revenue["Revenue"] = Tesla_Revenue['Revenue'].str.replace(',|\$',"",regex=True)
In [13]:
Tesla_Revenue.dropna(inplace=True)
Tesla_Revenue = Tesla_Revenue[Tesla_Revenue['Revenue'] != ""]
In [14]:
Tesla_Revenue.tail(5)
Out[14]:
| Date | Revenue | |
|---|---|---|
| 48 | 2010-09-30 | 31 |
| 49 | 2010-06-30 | 28 |
| 50 | 2010-03-31 | 21 |
| 52 | 2009-09-30 | 46 |
| 53 | 2009-06-30 | 27 |
Question 3: Use yfinance to Extract Stock Data
In [15]:
A=yf.Ticker("GME")
In [16]:
gme_data=A.history(period='max')
In [17]:
gme_data.reset_index(inplace=True)
gme_data.head()
Out[17]:
| Date | Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|---|
| 0 | 2002-02-13 00:00:00-05:00 | 1.620129 | 1.693350 | 1.603296 | 1.691667 | 76216000 | 0.0 | 0.0 |
| 1 | 2002-02-14 00:00:00-05:00 | 1.712707 | 1.716074 | 1.670626 | 1.683250 | 11021600 | 0.0 | 0.0 |
| 2 | 2002-02-15 00:00:00-05:00 | 1.683250 | 1.687458 | 1.658002 | 1.674834 | 8389600 | 0.0 | 0.0 |
| 3 | 2002-02-19 00:00:00-05:00 | 1.666418 | 1.666418 | 1.578047 | 1.607504 | 7410400 | 0.0 | 0.0 |
| 4 | 2002-02-20 00:00:00-05:00 | 1.615921 | 1.662210 | 1.603296 | 1.662210 | 6892800 | 0.0 | 0.0 |
Question 4: Use Webscraping to Extract GME Revenue Data
In [18]:
url1=("https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html")
html_data_2=requests.get(url1).text
In [19]:
soup1 = BeautifulSoup(html_data_2,'html.parser')
In [25]:
gme_table=pd.read_html(str(soup1))
print(f'Number of tables found:{len(gme_table)}')
gme_revenue=gme_table[1]
gme_revenue.columns=['Date','Revenue']
gme_revenue['Revenue']=gme_revenue['Revenue'].str.replace(',|\$','',regex=True)
gme_revenue.dropna(inplace=True)
gme_revenue = gme_revenue[gme_revenue['Revenue'] != ""]
gme_revenue.reset_index(drop=True,inplace=True)
print(gme_revenue.head())
Number of tables found:6
Date Revenue
0 2020-04-30 1021
1 2020-01-31 2194
2 2019-10-31 1439
3 2019-07-31 1286
4 2019-04-30 1548
In [26]:
gme_revenue.tail(5)
Out[26]:
| Date | Revenue | |
|---|---|---|
| 57 | 2006-01-31 | 1667 |
| 58 | 2005-10-31 | 534 |
| 59 | 2005-07-31 | 416 |
| 60 | 2005-04-30 | 475 |
| 61 | 2005-01-31 | 709 |
Question 5: Plot Tesla Stock Graph
In [27]:
make_graph(tesla_data, Tesla_Revenue, 'Tesla')
Question 6: Plot GameStop Stock Graph
In [28]:
make_graph(gme_data, gme_revenue, 'GameStop')
In [ ]: